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14 results for βcoastal upwellingβ
Data from: Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor.
<p><strong>Abstract</strong></p> <p>Long-term biological time series that monitor ecosystems across the ocean’s full water column are extremely rare. As a result, classic paradigms have yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multi-decadal time series spanning surface (0 m), midwater (200-1000 m), and benthic (~ 4000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increases in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), that dictate how long upwelling-driven changes persist within each community.</p> <p> </p> <p><strong>Data set description</strong></p> <p>This data set includes 3 files, one for each community. The files contain plankton biomass (for the surface community) or animal density (for midwater and benthos communities) as a function of sampling time and taxonomic group. </p> <ul> <li>surface.csv: autotrophic and heterotrophic surface plankton sampled in Monterey Bay by CTD-rosette and analyzed by epifluorescence microscopy and flow cytometry</li> <li>midwater.csv: midwater animals observed by ROV in the Monterey Bay mesopelagic zone from 200-1000m</li> <li>benthos.csv: benthic animals observed by ROV in a ~ 4000 m abyssal seafloor habitat at the base of the Monterey deep-sea fan</li> </ul> <p><strong>Detailed description </strong>(see additional details and references in <a href="https://www.pnas.org/doi/10.1073/pnas.2214567120">Messié et al., 2023</a>):</p> <p><strong>Surface time series:</strong> Plankton biomass was estimated from surface plankton counts collected using ship-based CTD-rosette at station M1 in Monterey Bay (122.022°W, 36.747°N). This station is part of a 3-station time series program operating in Monterey Bay since 1989 at 3-4 week intervals. Epifluorescence microscopy was used to enumerate and size auto- and heterotrophic plankton. Starting in 1998, flow cytometry samples provided more precise numbers for <em>Synechococcus</em> and eukaryotic picoplankton (<em>Prochlorococcus</em> was not included as no information is available prior to 1998). Standard geometric equations (e.g., ellipsoid, sphere, cylinder, pennate diatom shape) were used to calculate biovolumes of individual cells, and biomass of each plankton group was assessed using biovolume-based carbon conversions. For picoplankton an average value per cell was used: 82 fgC cell<sup>-1</sup> for <em>Synechococcus</em> and 530 fgC cell<sup>-1</sup> for eukaryotic picophytoplankton (red fluorescing picoplankton). Diatom biovolumes were converted to biomass using log<sub>10</sub>(Biomass) = 0.76 log<sub>10</sub>(Volume) - 0.29 where Biomass is in gC and Volume is in πm<sup>3</sup>. The ciliate conversion was Biomass = 0.08 * Volume. For all other plankton we used log<sub>10</sub>(Biomass) = 0.94 log<sub>10</sub>(Volume) - 0.6.</p> <p><strong>Midwater time series: </strong>Quantitative mesopelagic video transects were conducted at a single station in Monterey Bay (Midwater 1, 36°42′N, 122°02′W). The station is located over the axis of the Monterey Submarine Canyon, where the water column is approximately 1600 m deep. Data were collected using remotely operated vehicles (ROVs). Estimates of animal densities using ROV imaging underestimate some groups (notably fishes), but provide a more complete view of life in the ocean than traditional methods such as nets and acoustics, particularly for gelatinous animals. The ROVs conducted horizontal video transects while moving at about 0.5 m s<sup>-1</sup> for 10 min. Data for this paper come from approximately monthly transects made at 100 m intervals between 200 - 1000 m from 1997-2017. These years were chosen because the entire mesopelagic water column was more evenly surveyed than in the years prior. In each transect, the community of animals was annotated by professional annotators using the open-source Video Annotation and Referencing System (VARS) software. Annotators identified organisms in transect video to the lowest taxon possible; in many cases to species. We selected 63 taxonomic groups defined at the highest possible taxonomic resolution; annotations not included represent 31% of the total (84% of which are euphausiids, chaetognaths, and unidentified appendicularians). Calibrated cameras on MBARI ROVs and accurate measurement of ROV speed through water, allow for the calculation of volume for each transect. Animal density was calculated for each taxonomic group and each depth-specific transect as the number of individuals divided by the corresponding transect volume, further averaged over the water column from 200 - 1000 m. Midwater transecting methods and their efficacy are well-documented.</p> <p><strong>Benthic time series: </strong>Two comparable methods were used to assess benthic communities at Station M (34°50′N, 123°00′W). From 1989-2005, the identification to the lowest possible taxon, and quantity of benthic animals were recorded from images taken by a camera-sled towed along a horizontal transect above the sea floor at a speed of approximately 1 m s<sup>-1</sup>, taking a film image every 4-5 seconds (water depth ~ 4,100 m). The developed film was projected by a Beseler model 23C-II enlarger for annotation of identifiable animals in images. From 2006-2018, benthic communities were assessed using ROV video transects recorded from approximately 1.3 m above the sea floor, with a view of approximately 1 m wide, and length typically approximately 1 km. Water depth for these transects was approximately 4,000 m, the lower depth limit of the ROV. Animals visible in the video were identified and annotated using VARS. The 2006 change in sampling method and in time series location and depth was found to have little impact on the megafauna time series. </p>
A Lagrangian study of the contribution of the Canary coastal upwelling to the nitrogen budget of the open North Atlantic
<p>The attached datasets constitute the particle trajectory data produced in the experiment for Hailegeorgis et al..</p> <p>The "traj_upwell_1d_70m_1d-variables.nc" contains variables that describe different aspects of each upwelled particle (mostly regarding a particle's release or its initial or final conditions).</p> <p>The rest of the files with the format "traj_upwell_1d_70m_XXX-traj.nc" describe an attribute XXX (location or nutrient concentration) along the trajectory of upwelled particles tracked as part of the experiment.</p> <p>With ARIANE, particles are released and tracked in a ROMS simulation of the Canary coastal upwelling region. Out of the ~10M particles, the trajectories of the ~353K (~3.6%) that upwell are included. The variable "index_in_full_exp" in file "traj_upwell_1d_70m_1d-variables.nc" shows the index of each of these upwelling particles in the larger pool of released particles. For each upwelled particle, out of the 720-day trajectories starting from its release into the coast, the values from its upwelling step to its exit from the experiment are included, with the values outside this range being filled with a generic value (1.e20). An upwelled particle exits the experiment when it leaves the regional ROMS simulation altogether or when it leaves the coast and returns to the coast to re-upwell (more details in the paper).</p> <p>Be mindful of the different values of time. In "traj_upwell_1d_70m_1d-variables.nc", the variable "release_time" tells each particle's release time, in days since onset of the ROMS simulation, while variable "coast_exit_time" tells each particle's day of exiting coast, in days since its release. In each particle's trajectory (in traj_lon, traj_lat, etc), the first and last steps with valid values are the same as the days of its upwelling and its exit, respectively, since its release.</p> <p>The files contain the name and description of each variable. Along with the details in the publication, the descriptions here should be enough to fully interpret the information and replicate our analysis.</p>
Dataset from "Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone"
<p>Dataset of dissolved noble gas (He, Ne, Ar, Kr, and Xe) measurements in Monterey Bay, CA. Published as a supplement to: Manning, C.C., R.H.R. Stanley, D.P. Nicholson, and M.E. Squibb (2016). Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone. <em>IOP Conference Series: Earth and Environmental Science</em>, 35, 012017 (13 pp). doi: 10.1088/1755-1315/35/1/012017</p>
Decoupling silicon metabolism from carbon and nitrogen assimilation poises diatoms to exploit episodic nutrient pulses in a coastal upwelling system
<p>Diatoms serve as the major link between the marine carbon (C) and silicon (Si) biogeochemical cycles through their contributions to primary productivity and requirement for Si during cell wall formation. Although several culture-based studies have investigated the molecular response of diatoms to Si and nitrogen (N) starvation and replenishment, diatom silicon metabolism has been understudied in natural populations. A series of deckboard Si-amendment incubations were conducted using surface water collected in the California Upwelling Zone near Monterey Bay. Steep concentration gradients in macronutrients in the surface ocean coupled with substantial N and Si utilization led to communities with distinctly different macronutrient states: replete ('healthy'), low N ('N-stressed'), and low N and Si ('N- and Si-stressed'). Biogeochemical measurements of Si uptake combined with metatranscriptomic analysis of communities incubated with and without added Si were used to explore the underlying molecular response of diatom communities to different macronutrient availability. Metatranscriptomic analysis revealed that N-stressed communities exhibited dynamic shifts in N and C transcriptional patterns suggestive of compromised metabolism. Expression patterns in communities experiencing both N and Si stress imply that the presence of Si stress may partially ameliorate N stress and dampen the impact on organic matter metabolism. This response builds upon previous observations that the regulation of C and N metabolism is decoupled from Si limitation status, where Si stress allows the cell to optimize the metabolic machinery necessary to respond to episodic pulses of nutrients. Several well-characterized Si-metabolism associated genes were found to be poor molecular markers of Si physiological status; however, several uncharacterized Si-responsive genes were revealed to be potential indicators of Si stress or silica production.</p>
Decoupling silicon metabolism from carbon and nitrogen assimilation poises diatoms to exploit episodic nutrient pulses in a coastal upwelling system
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Data for: Wintertime coastal upwelling in Lake Geneva: An efficient transport process for deep-water renewal in a large, deep lake
<p>Combining field measurements, 3D numerical modeling and Lagrangian particle tracking,<br> we investigated wind-driven, Ekman-type coastal upwelling during the weakly stratified winter period 2017/2018<br> in Lake Geneva, a large and deep lake in western Europe. The data include measurements from<br> moored Acoustic Doppler Current Profilers (ADCP) and vertical temperature profiles<br> along with the corresponding 3D modeling and particle tracking results.<br> The three-dimensional model used in this study is based on the MIT General<br> Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).<br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118).</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Coastal upwelling may strengthen the controls of herbivory and light over the population dynamics of Hedophyllum sessile in the Oregon rocky intertidal
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Local-scale patterns of coastal hypoxia in an upwelling region
<p>GENERAL INFORMATION</p> <p>Title of Dataset: Replication data for: </p> <p>- Sea Surface Temperature (SST, daily) MODIS-Aqua (2016-2019)</p>
On the pathways of wind-driven coastal upwelling: nonlinear momentum flux and baroclinic instability
<p>The data that support the findings in "On the pathways of wind-driven coastal upwelling: nonlinear momentum flux and baroclinic instability"</p>
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PAPAGAYO, COSTA RICA V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Papagayo, Costa Rica dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Papagayo, Costa Rica. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF TEHUANTEPEC, MEXICO V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Tehuantepec, Mexico dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Tehuantepec, Mexico. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PANAMA, PANAMA V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Panama, Panama dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Panama, Panama. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
Effects of Stratification on Wind-Driven Upwelling over a Coastal Valley
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